Landslide Hazard Zonation and Slope Instability Assessment using Optical and InSAR Data: A Case Study from Gidole Town and its Surrounding Areas, Southern Ethiopia

2019 ◽  
Vol 3 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Filagot Mengistu ◽  
K.V. Suryabhagavan ◽  
Tarun Kumar Raghuvanshi ◽  
Elias Lewi
Keyword(s):  
Information Value ◽  
Slope Instability ◽  
Southern Ethiopia ◽  
Hazard Zonation ◽  
The Past ◽  
Causative Factors ◽  
Information Value Model ◽  
High Hazard ◽  
Value Model ◽  
Very High

The present study was carried out in and around Gidole Town in Southern Ethiopia which is about 580km from Addis Ababa. The main objective of the study was to prepare a landslide hazard zonation (LHZ) map by using Bivariat statistical information value model and to assess the slope instability in the area by using InSAR approach. For LHZ six causative factors such as; slope, land-use/land-cover, slope-material, elevation, aspect, and Normalized Difference Vegetation Index (NDVI) were considered. For the sub-classes of these causative factors weights were obtained from the information value model. The results showed that very high hazard zones and high hazard zones covers 6.63% (14.12 km2) and 15.36% (32.72 km2) of the area, respectively. Whereas, moderate hazard, low hazard and very low hazard zones covers 7.47% (15.9 km2), 34.2% (72.85 km2) and 36.34% (77.4 km2) of the area, respectively. Further, validation of the LHZ map showed that 92.3% of the past landslides fall in very high hazard and high hazard zones. Thus, the hazard zones delineated in the present study has reasonably validated with the past landslide data and the potential zones depicted in the prepared LHZ map can be applied for the safe planning of the area. Further, the results of the PS-InSAR processing indicates that the average downward displacement in the study area is gradually increasing from 15.3mm/yr (2014) to −19.2 mm/yr (2018) and the rate of displacement in general increases with increase in the average monthly precipitation at all selected persistence scattered points.

Landslide hazard zone mapping using Information Value model: the case of Gidole Landslide, Southern Ethiopia

2020 ◽  
Author(s):  
Filagot Mengistu Walle ◽  
Karuturi Venkata Suryabhagavan ◽  
Tarun Raghuvanshi ◽  
Elias Lewi
Keyword(s):  
Causative Factor ◽  
Landslide Hazard ◽  
Information Value ◽  
Southern Ethiopia ◽  
Causative Factors ◽  
Hazard Zone ◽  
Information Value Model ◽  
High Hazard ◽  
Value Model ◽  
Factor Maps

<p>Landslide hazard is becoming serious environmental constraints for the developmental activities in the highlands of Ethiopia. With the current infrastructure development, urbanization, rural development, and with the present landslide management system, it is predictable that the frequency and magnitude of landslide and losses due to such hazards would continue to increase. In the present study landslide hazard zone mapping were carried out in and around Gidole Town in Southern Ethiopia. The main objective of the study was to map landslide hazard zone using Information Value Bi-variant statistical model.  For landslide hazard zonation of the study area six causative factors namely; aspect, slope angle, elevation, Lithology, Normalized Deference Vegetation Index (NDVI) and land-use and land-cover were considered. The landslide inventory mapping for the present study area was carried out through field observations and Google Earth image interpretation. Later, Information value was calculated based on the influence of causative factors on past landslide. The distribution of landslide over each causative factor maps was obtained and analyzed. Weights for the class with in these causative factor maps was obtained using information value model. Distribution of landslide in the study area was largely governed by aspect of southwest facing, slope angel of 30-45<sup>o</sup>, elevation of 1815–2150m, NDVI of 0.27−0.37, Lithology of colluvial deposit and land-use and land-cover of agricultural land. The landslide hazard zonation map shows that 78.38km<sup>2</sup> (36.3%) area fall within very low hazard (VLH) zone, 72.85km<sup>2</sup> (34.2%) of the area fall within low hazard (LH) zone, 12.78 km<sup>2</sup> (6.6%), 32.72 km<sup>2</sup> (15.4%) and 15.89 km<sup>2</sup> (7.5%) of the area falls into very high hazard (VHH), high hazard (HH) and moderate hazard (MH), respectively. Further, validation of LHZ map with past landslide inventory data shows that 92.3% of the existing landslides fall in very high hazard (VHH) and high hazard (VHH) zone. Thus, it can safely be concluded that the hazard zones delineated in the present study validates with the past landslide data and the potential zone depicted can reasonably be applied for the safe planning of the area.</p><p><strong>Key words</strong>: Landslide, Gidole, Landslide hazard zone, Information Value model</p>

The Landslide Susceptibility Assessment using Bi-variate Statistical Information Value Model of Chenab River Valley, Jammu and Kashmir (India)

2021 ◽  
Vol 14 (11) ◽  
pp. 44-56
Author(s):  
Abhijit S. Patil ◽  
Bidyut K. Bhadra ◽  
Sachin S. Panhalkar ◽  
Sudhir K. Powar
Keyword(s):  
Landslide Susceptibility ◽  
Statistical Information ◽  
River Valley ◽  
Information Value ◽  
Landslide Inventory ◽  
Susceptibility Map ◽  
Acceptable Result ◽  
Information Value Model ◽  
Value Model ◽  
Chenab River

Almost every year, the Himalayan region suffers from a landslide disaster that is directly associated with the prosperity and development of the area. The study of landslide disasters helps planners, decision-makers and local communities for the development of anthropogenic structures in order to enhance the safety of society. Therefore, the prime aim of this research is to produce the landslide susceptibility map for the Chenab river valley using the bi-variate statistical information value model to detect and demarcate the areas of potential landslide incidence. The object-based image analysis method identified about 84 potential sites of landslides as landslide inventory. The statistical information value model is derived from the landslide inventory and multiple causative factors. The outcome showed that 23% area of the Chenab river valley falls into the class of a very high landslide susceptibility zone. The ROC curve method is used to validate the model which denoted the acceptable result for the landslide susceptibility zonation with 0.826 AUC value for the Chenab river valley.

scholarly journals Landslide Hazard Zonation and Evaluation Around Debre Markos Town, NW Ethiopia—A GIS-Based Statistical Approach

2021 ◽  
Author(s):  
Dawit Asmare Manderso
Keyword(s):  
Research Area ◽  
Landslide Hazard ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Zonation Map ◽  
Hazard Zone ◽  
High Hazard ◽  
Debre Markos ◽  
Moderate Hazard ◽  
Very High

Abstract The main goal of this research was to perform a landslide hazard zonation and evaluation around Debre Markos town, North West Ethiopia, found about 300 km from the capital city Addis Ababa. To achieve the aim, a GIS-based probabilistic statistical technique was used to rate the governing factors, followed by geoprocessing in the GIS setting to produce the landslide hazard zonation map. In this research, eight internal causative and external triggering factors were selected: slope material (lithology and soil mass), elevation, aspect, slope, land use land cover, curvature, distance to fault, and distance to drainage. Data were collected from field mapping, secondary maps, and digital elevation models. Systematic and detailed fieldwork had been done for image interpretation and inventory mapping. Accordingly, the past landslides map of the research area was prepared. All influencing factors were statistically analyzed to determine their relationship to previous landslides. The results revealed that 17.15% (40.60 km2), 25.53% (60.45 km2), 28.04% (66.39 km2), 18.93% (44.83 km2), and 10.36% (24.54 km2) of the research area falls under no hazard, low hazard, moderate hazard, high hazard, and very high hazard respectively. The validation of the landslide hazard zonation map reveals that 1%, 2%, 3%, and 94% of past landslides fall in no hazard zone, low hazard, moderate hazard zone, and high hazard or very high hazard zones respectively. The validation of the landslide hazard zonation map thus, it has been adequately demonstrated that the adopted approach has produced acceptable results. The defined hazard zones can practically be utilized for land management and infrastructure construction in the study area.

An Empirical Development of Knowledge and Information Value Model

2009 ◽  
Vol 40 (1) ◽  
pp. 113-132
Author(s):  
Seung-Hoon Yoo ◽  
Jae-Yong Heo ◽  
Yoon-Gih Ahn
Keyword(s):  
Information Value ◽  
Information Value Model ◽  
Value Model

Realization of the Multi-Factor Combined Information Value Model in the Spatial Prediction of Landslides

2013 ◽  
Vol 734-737 ◽  
pp. 3163-3170
Author(s):  
Yu Feng Chen ◽  
Xue Lian Cao
Keyword(s):  
Influence Factors ◽  
Great Influence ◽  
Drainage System ◽  
Spatial Prediction ◽  
Information Value ◽  
Slope Aspect ◽  
Strongly Correlated ◽  
Information Value Model ◽  
Value Model ◽  
The Ideal

Information value model simplify the total information of evaluation unit down to sum of each factor, what may influence the prediction accuracy when factors are strongly correlated . Thus, its better to select the original modelthe multi-factor combined information value model, which directly calculate the information of factors-combination. However, the calculation is hard to realize due to the large number of combinations. In this paper, we propose a method that can quickly calculate the information. Taking Badong area for example, selecting slope, aspect, lithology, distance to drainage system and distance to road as influence factors, constructed the ideal information value model and the multi-factor combined information value model respectively. We found that the former model accuracy is 71.1%, with the latter is 80.3%. The result proved that the correlation between factors may have great influence, and showed the multi-factor combined information value model is better in a way.

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